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Intelligent Clinical Data Mining for Early Disease Prediction using Multi-Modal Patient Records

Paper ID:
IJSDIP_2023_VOLUME 11_ISSUE 1 _210-218
Author's Name:
Katta Padmaja
Volume:
11
Issue:
Issue 1
Year:
2023
Pages:
210-218

Abstract

The exponential growth of healthcare data from diverse sources such as electronic health records (EHRs), laboratory reports, medical imaging, and sensor-based monitoring has created an opportunity to revolutionize disease diagnosis through intelligent data mining. However, the heterogeneous and high-dimensional nature of these multi-modal datasets poses a major challenge to traditional predictive models. This study proposes an intelligent clinical data mining framework for early disease prediction by integrating multi-modal patient records. The framework leverages a hybrid architecture combining deep learning and classical data mining techniques to extract and fuse relevant features from structured, unstructured, and visual data. Textual data from clinical notes are processed using Bidirectional Encoder Representations from Transformers (BERT) for contextual understanding, while medical images are analyzed using Convolutional Neural Networks (CNNs) to capture spatial features. Tabular and numerical data are mined using feature selection and dimensionality reduction techniques such as PCA and Random Forest importance ranking. The extracted features are then unified through a feature-level fusion strategy and classified using a hybrid ensemble model to predict disease onset. The system is evaluated using publicly available datasets such as MIMIC-III and NIH Chest X-Ray, achieving improved accuracy, recall, and F1-score compared to unimodal approaches. Furthermore, explainability tools such as SHAP and LIME are incorporated to interpret model predictions and enhance clinical transparency. The proposed model demonstrates significant potential for assisting clinicians in early disease diagnosis, reducing diagnostic delays, and enabling personalized healthcare interventions through intelligent, interpretable data mining.

Keywords

Data Mining; Multi-Modal Learning; Early Disease Prediction; Electronic Health Records (EHR); Deep Learning; Clinical Decision Support System; Feature Fusion; Explainable AI; Biomedical Informatics; Healthcare Analytics.

References

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